Pseudo two-dimensional shape normalization methods for handwritten Chinese character recognition

Abstract

The nonlinear normalization (NLN) method based on line density equalization is popularly used in handwritten Chinese character recognition. To overcome the insufficient shape restoration capability of one-dimensional NLN, a pseudo two-dimensional NLN (P2DNLN) method has been proposed and has yielded higher recognition accuracy. The P2DNLN method, however, is very computationally expensive because of the line density blurring of each row/column. In this paper, we propose a new pseudo 2D normalization method using line density projection interpolation (LDPI), which partitions the line density map into soft strips and generate 2D coordinate mapping function by interpolating the 1D coordinate functions that are obtained by equalizing the line density projections of these strips. The LDPI method adds little computational overhead to one-dimensional NLN yet performs comparably well with P2DNLN. We also apply this strategy to extending other normalization methods, including line density projection fitting, centroid-boundary alignment, moment, and bi-moment methods. The latter three methods are directly based on character image instead of line density map. Their 2D extensions provide real-time computation and high recognition accuracy, and are potentially applicable to gray-scale images and online trajectories.